Classifying with scikit-learn Estimators | Learning Data Mining with Python

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Learning Data Mining with Python

By : Robert Layton

3.7 (7)

Learning Data Mining with Python

3.7 (7)

By: Robert Layton

Overview of this book

If you are a programmer who wants to get started with data mining, then this book is for you.

Learning Data Mining with Python

Learning Data Mining with Python

Credits

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Preface

Free Chapter

Getting Started with Data Mining

Getting Started with Data Mining

Introducing data mining

Using Python and the IPython Notebook

A simple affinity analysis example

A simple classification example

What is classification?

Summary

Classifying with scikit-learn Estimators

Classifying with scikit-learn Estimators

scikit-learn estimators

Preprocessing using pipelines

Pipelines

Summary

Predicting Sports Winners with Decision Trees

Predicting Sports Winners with Decision Trees

Loading the dataset

Decision trees

Sports outcome prediction

Random forests

Summary

Recommending Movies Using Affinity Analysis

Recommending Movies Using Affinity Analysis

Affinity analysis

The movie recommendation problem

The Apriori implementation

Extracting association rules

Summary

Extracting Features with Transformers

Extracting Features with Transformers

Feature extraction

Feature selection

Feature creation

Creating your own transformer

Summary

Social Media Insight Using Naive Bayes

Social Media Insight Using Naive Bayes

Disambiguation

Text transformers

Naive Bayes

Application

Summary

Discovering Accounts to Follow Using Graph Mining

Discovering Accounts to Follow Using Graph Mining

Loading the dataset

Finding subgraphs

Summary

Beating CAPTCHAs with Neural Networks

Beating CAPTCHAs with Neural Networks

Artificial neural networks

Creating the dataset

Training and classifying

Improving accuracy using a dictionary

Summary

Authorship Attribution

Authorship Attribution

Attributing documents to authors

Function words

Support vector machines

Character n-grams

Using the Enron dataset

Summary

Clustering News Articles

Clustering News Articles

Obtaining news articles

Extracting text from arbitrary websites

Grouping news articles

Clustering ensembles

Online learning

Summary

Classifying Objects in Images Using Deep Learning

Classifying Objects in Images Using Deep Learning

Object classification

Application scenario and goals

Deep neural networks

GPU optimization

Setting up the environment

Application

Summary

Working with Big Data

Working with Big Data

Big data

Application scenario and goals

MapReduce

Application

Summary

Next Steps…

Next Steps…

Chapter 1 – Getting Started with Data Mining

Chapter 2 – Classifying with scikit-learn Estimators

Chapter 3: Predicting Sports Winners with Decision Trees

Chapter 4 – Recommending Movies Using Affinity Analysis

Chapter 5 – Extracting Features with Transformers

Chapter 6 – Social Media Insight Using Naive Bayes

Chapter 7 – Discovering Accounts to Follow Using Graph Mining

Chapter 8 – Beating CAPTCHAs with Neural Networks

Chapter 9 – Authorship Attribution

Chapter 10 – Clustering News Articles

Chapter 11: Classifying Objects in Images Using Deep Learning

Chapter 12 – Working with Big Data

More resources

Index

Index

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Summary

In this chapter, we used several of scikit-learn's methods for building a standard workflow to run and evaluate data mining models. We introduced the Nearest Neighbors algorithm, which is already implemented in scikit-learn as an estimator. Using this class is quite easy; first, we call the fit function on our training data, and second, we use the predict function to predict the class of testing samples.

We then looked at preprocessing by fixing poor feature scaling. This was done using a Transformer object and the MinMaxScaler class. These functions also have a fit method and then a transform, which takes a dataset as an input and returns a transformed dataset as an output.

In the next chapter, we will use these concepts in a larger example, predicting the outcome of sports matches using real-world data.

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